Machine Learning Reading Group Meetings at Columbia the difficulty of objectively evaluating how well members of a given a pathway or functional class of interest Gene Set are represented in the markers list To address this problem we introduce a statistical methodology called Gene Set Enrichment Analysis GSEA for determining whether a given Gene Set is over represented or enriched in a Gene List of markers ordered by their correlation with a phenotype or class distinction of interest The method is based upon a score computed as the maximum deviation of a random walk in the same spirit as the Kolmogorov Smirnov statistic and uses permutation testing to assess significance When multiple Gene Sets are tested simultaneously we propose two approaches to address the multiplicity Validation GSEA which controls the Familywise error rate FWER and Discovery GSEA which controls the False Discovery rate FDR The utility of this procedure will be illustrated on two biological problems validating a mouse model of lung cancer and finding chromosomal dislocations for myeloid leukemia April 2 2004 Blei Griffiths Jordan and Tenenbaum Hierarchical Topic Models and the Nested Chinese Restaurant Process Risi presenting Slides ps pdf March 25 2004 Naftali Tishby The Hebrew University Efficient data representations that preserve relevant information A new look at Shannon s Information Theory 11 00am Interschool Lab In this talk I will take a new look at Shannon s Information theory from the Machine Learning perspective I will argue that Shannon s theory provides a compelling mechanism for quantifying the fundamental tradeoff between complexity and accuracy by unifying the source and channel coding theorems into one principle which I call the Information Bottleneck IB This unified view of the coding theorems can shed new light on the question of relevant data representation and suggests new algorithms for extracting such representations from co occurrence statistics It also provides new ways of thinking about neural coding and neural data analysis When applied to the analysis of human language it reveals new possibly universal scaling law that may reflect the way words are acquired in natural languages The IB principle has new interesting extensions that deal with multivariate data using Graphical models and multi information allow adding irrelevant side information and extract nonlinear continuous dimension reduction that preserve information SDR I will describe some of those extensions as time allows More information and many related papers can be found at http www cs huji ac il labs learning Papers IBM list html March 12 2004 Brendan J Frey A Principled and Computationally Efficient Approach to Visual and Auditory Scene Analysis 11 00am Interschool Lab Scene analysis is currently the most exciting problem to work on in sensory processing Scene analysis entails decomposing sensory inputs into a combinatorial explanation that can be used to predict new inputs The problem is old and can be traced back to Helmholtz and more fuzzily to the ancient Greeks who thought that fire etc was the combinatorial explanation However only recently do we have 1 Efficient algorithms that turn exponential time combinatorial inference and learning algorithms into linear time approximate algorithms and 2 Fast computers that can process sensory data repeatedly and quickly enough that grad students stick around to look at the results In this talk I ll describe research on visual and auditory scene analysis being done in my group at the University of Toronto PSI Lab using graphical models and efficient approximate inference algorithms The focus will be on the generative modeling approach and why this approach holds the most promise for solving the problem of sensory scene analysis For serious reading check out the following tutorial paper http www psi toronto edu frey stuff tutorial ps gz For fun videos audio clips gene expression array results etc see the web pages of Nebojsa Jojic my postdocs Romer Rosales Quaid Morris and my current students Kannan Achan Anitha Kannan Chris Pal March 5 2004 John Langford The Method of Reduction in Machine Learning Reductions transform a solver for one domain into a solver for another domain I will discuss a general framework for analyzing reductions between learning domains which captures Boosting ECOC and other well known algorithms I will also present new algorithmic reductions and empirical tests of their performance February 27 2004 Yoav Freund Robert E Schapire Yoram Singer Manfred K Warmuth Using and combining predictors that specialize Ray presenting February 6 2004 Denis V Chigirev William S Bialek Optimal Manifold Representation of Data An Information Theoretic Approach Andy presenting January 30 2004 Vishy SVN Vishwanathan 2 30pm in CEPSR 6LE5 We present Hilbert space embeddings of dynamical systems and embeddings generated via dynamical systems This is achieved by following the behavioural framework invented by Willems namely by comparing trajectories of states As important special cases we recover the diffusion kernels of Kondor and Lafferty generalised versions of directed graph kernels of Gartner novel kernels on matrices and new similarity measures on Markov Models We show applications of our method to Dynamical texture recognition problems from computer vision January 27 2004 Eleazar Eskin The Homology Kernel A Biological Motivated Sequence Embedding 4 30pm in CS Lounge Many recent techniques in learning over biological sequences implicitly embed sequences into a Euclidean space in order to take advantage of strong margin based learning algorithms However these embeddings often do not take advantage of the rich biological intuitions that have motivated development of Hidden Markov Model style biological sequence models and have lead to great successes in computational biology In this talk we present a new biological motivated sequence embedding We discuss several of the formal properties of the embedding include its connection to local sequence alignment One of the key features of the embedding is that a sequence is embedded along with its homologues or neighboring sequences The distance between two sequences is defined by the distance between close neighbors of the sequences We demonstrate application of the embedding to several applications We apply the embedding to learning protein secondary structure and protein family classification We also show how the embedding

Links Intelligence and Statistics AISTAT Uncertainty and Artificial Intelligence UAI Conference on Learning Theory COLT European Conference on Machine Learning ECML International Conference on Machine Learning and Applications ICMLA As well as these vision conferences occasionaly Computer Vision and Pattern Recognition CVPR International Conference on Computer Vision ICCV European Conference on Computer Vision ECCV The main journals we are interested in include Machine Learning MLJ Journal of Machine Learning Research JMLR

David M. Blei I wrote a general introduction to topic modeling John Lafferty and I wrote a more technical review paper about this field Here are slides from some of my recent talks about topic modeling Probabilistic Topic Models 2012 ICML Tutorial Probabilistic Topic Models 2012 Machine Learning Summer School 211 slides Probabilistic Topic Models Origins and Challenges 2013 Topic Modeling Workshop at NIPS Here is a video from a talk on dynamic

index.html prerequisite course is Foundations of Graphical Models and you should be comfortable with its material Specifically you should be able to write down a new model where each complete conditional is in the exponential family derive and implement an approximate inference algorithm for the model and understand how to interpret its results You should also be fluent in the semantics of graphical models Finally note this is a seminar It